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Open business intelligence: on the importance of data quality awareness in user-friendly data mining

By Jose-Norberto Mazón, Jose Zubcoff, Irene Garrigós, Roberto Espinosa and Rolando Rodríguez


Citizens demand more and more data for making decisions in their daily life. Therefore, mechanisms that allow citizens to understand and analyze linked open data (LOD) in a user-friendly manner are highly required. To this aim, the concept of Open Business Intelligence (OpenBI) is introduced in this position paper. OpenBI facilitates non-expert users to (i) analyze and visualize LOD, thus generating actionable information by means of reporting, OLAP analysis, dashboards or data mining; and to (ii) share the new acquired information as LOD to be reused by anyone. One of the most challenging issues of OpenBI is related to data mining, since non-experts (as citizens) need guidance during preprocessing and application of mining algorithms due to the complexity of the mining process and the low quality of the data sources. This is even worst when dealing with LOD, not only because of the different kind of links among data, but also because of its high dimensionality. As a consequence, in this position paper we advocate that data mining for OpenBI requires data quality-aware mechanisms for guiding non-expert users in obtaining and sharing the most reliable knowledge from the available LOD

Topics: Linked open data (LOD), User-friendly data mining, Data quality-aware mechanisms, Lenguajes y Sistemas Informáticos, Estadística e Investigación Operativa
Publisher: 'Association for Computing Machinery (ACM)'
Year: 2012
DOI identifier: 10.1145/2320765.2320812
OAI identifier:

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